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POWER-Q2300 is intended for the temporary relief of minor muscle aches and pains, and for temporary increase in blood circulation to the treated areas in people who are in good health. POWER-Q2300 simulates kneading of tissues by using an inflatable garment (cuff).

POWER-Q2300 is comprised of a main body, a power cord, cuffs, and hoses for connecting the device to the cuffs. The device is AC-powered. There are four cuffs to apply to different body areas, such as leg, arm, hip, and half-leg. The cuffs have 4-chambers. The cuff inflates and deflates sequentially to apply the pressure on the target body areas which are controlled by the main body. Double hose is to connect main body and leg cuff. Single hose is to connect main body and arm cuff (or hip cuff or half-leg cuff). Extension zipper is for bigger leg. Blocked jack is to block an air outlet that will not be used. The colors of the cuffs and extension zipper are available in pink or gray.

The provided document is a 510(k) summary for the "Compressible Limb and Circulation Therapy System, Model POWER-Q2300". This type of document is a premarket notification to the FDA to demonstrate that a device is substantially equivalent to a legally marketed predicate device.

Based on the information provided in this 510(k) summary, the device under review is a physical medical device (a massager) aiming for "temporary relief of minor muscle aches and pains, and for temporary increase in blood circulation". It is NOT an AI/ML-enabled diagnostic or therapeutic device. Therefore, many of the typical acceptance criteria and study components requested in the prompt (such as accuracy metrics for an AI model, ground truth establishment by experts, MRMC studies, training/test set sample sizes for AI, etc.) are not applicable to this type of device.

The "study that proves the device meets the acceptance criteria" for a physical device like this primarily involves non-clinical performance testing to ensure it adheres to recognized standards for safety and essential performance, and a comparison to a predicate device to demonstrate substantial equivalence.

Here's an attempt to answer the prompt based only on the provided document, highlighting which aspects are applicable and which are not:

Acceptance Criteria and Device Performance for POWER-Q2300

As this is a physical medical device and not an AI/ML model, the acceptance criteria are based on meeting recognized medical electrical equipment standards and demonstrating comparable performance to a predicate device for its intended physical function.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

Not applicable in the context of AI/ML testing. For this physical device, "testing" refers to non-clinical laboratory verification against engineering standards. The document states: "Performance testing for pressure accuracy, seam strength, and fail mode testing has been performed." It does not specify sample sizes for these tests, as they would typically involve engineering samples tested to specific physical and electrical standards, not a "test set" of patient data.

Data provenance: Not applicable. There is no patient data or clinical data used for regulatory submission, as explicitly stated: "No clinical testing was performed."

3. Number of experts used to establish the ground truth for the test set and qualifications of those experts

Not Applicable. As there was no clinical testing performed and no AI/ML component, there was no need for expert ground truth establishment using patient data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

Not Applicable. This is relevant for clinical studies or AI algorithm validation with uncertain ground truth. For this physical device, regulatory clearance relied on non-clinical engineering tests and comparison to a predicate device.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

Not Applicable. This device is a physical massager, not an AI-assisted diagnostic or therapeutic tool. Therefore, MRMC studies are not relevant.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

Not Applicable. There is no AI algorithm to evaluate in standalone mode.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

Not Applicable for clinical ground truth. The "ground truth" for this device's performance validation is its adherence to recognized engineering standards (IEC, ANSI/AAMI) for electrical safety, EMC, usability, and specific physical performance parameters (pressure accuracy, seam strength, fail mode). There is no "patient-specific" ground truth required for this type of device clearance by FDA.

8. The sample size for the training set

Not Applicable. This device does not use an AI or machine learning model, and therefore has no "training set."

9. How the ground truth for the training set was established

Not Applicable. As there is no AI model or training set, there is no ground truth establishment for a training set.

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